AI Mock Interview for Data Engineers

Practice with 3 specialized AI agents trained on 1,863+ real interview questions from Amazon, Google, Databricks, and 95+ more companies.

Start Free AI Interview →Browse 1,863+ Questions

Free: 5 messages/session · No credit card required

How the AI Mock Interview Works

Three AI agents, each specialized for a different phase of your interview preparation.

Interviewer Agent

FAANG Bar Raiser

✓Asks real FAANG-caliber questions one at a time
✓Pushes depth with adaptive follow-up questions
✓Delivers a hire/no-hire verdict every 3 questions
✓Covers SQL, Spark, System Design, Python, Cloud, Behavioral

Sample Session

Let's start with a common SQL round question. Explain the difference between ROW_NUMBER(), RANK(), and DENSE_RANK() window functions.

ROW_NUMBER assigns a unique sequential number. RANK assigns the same rank for ties but skips. DENSE_RANK doesn't skip...

Good foundation. Follow-up: You have a table with 10M rows and need to find the top 3 salaries per department. Which function would you use and why? Consider performance at scale.

Evaluator Agent

Hiring Committee

✓Scores answers on 4 axes: Accuracy, Depth, Communication, Completeness
✓Provides detailed gap analysis vs. expert benchmark
✓Shows the ideal 10/10 answer with code examples
✓Delivers Strong Hire / Hire / Lean Hire / No Hire verdict

Sample Session

Here's my answer to 'How do you handle data skew in Spark?': I would use salting to redistribute data...

Accuracy: 8/10 | Depth: 6/10 | Communication: 8/10 | Completeness: 5/10 Gap: You missed broadcast joins for small-large table joins, and didn't mention AQE (Adaptive Query Execution). Overall: Lean H...

Explainer Agent

Staff Engineer Mentor

✓Breaks complex concepts into clear, interview-ready explanations
✓Provides production-scale code examples
✓Gives interview answer templates you can use verbatim
✓Prepares follow-up questions the interviewer will likely ask

Sample Session

Explain the medallion architecture in a data lakehouse

Think of it as a factory assembly line: - Bronze (Raw): Raw data lands as-is from sources - Silver (Cleaned): Schema enforcement, deduplication, type casting - Gold (Business): Aggregated, joined, bu...

Topics Covered in AI Interviews

Our AI agents are trained on every major data engineering interview domain.

487+

SQL & Databases

Window functions, CTEs, query optimization, BigQuery, Redshift

452+

Apache Spark

RDDs, DataFrames, Delta Lake, performance tuning, data skew

179+

System Design

Pipeline architecture, batch vs stream, medallion, fault tolerance

179+

Python & Coding

PySpark, pandas, algorithms, data structures, OOP patterns

179+

Cloud & Tools

AWS Glue, GCP Dataflow, Airflow, Snowflake, Terraform

144+

Behavioral

STAR method, leadership, conflict resolution, career goals

Frequently Asked Questions

How does the AI mock interview work?

You choose one of three AI agents and start a conversation. The Interviewer agent asks real FAANG-caliber questions, follows up on your answers, and delivers a hire/no-hire verdict. The Evaluator scores your answers on a 10-point scale across accuracy, depth, communication, and completeness. The Explainer teaches you concepts with production examples and code.

What makes this different from ChatGPT for interview prep?

Our agents are specifically trained on 1,800+ real data engineering interview questions from Amazon, Google, Databricks, and 94+ companies. They follow structured interview protocols, score against expert-calibrated benchmarks, and provide company-specific feedback. ChatGPT gives generic answers; our agents simulate the real thing.

Is the AI mock interview free?

Yes. Free users get 5 AI messages per session across all 3 agents. Pro subscribers get 100 messages per day with access to all agents, mock interviews, and the full 1,800+ question database with expert answers.

What topics does the AI interviewer cover?

All major data engineering domains: SQL (window functions, CTEs, optimization), Apache Spark (RDDs, DataFrames, tuning), System Design (pipelines, batch vs streaming, lakehouse), Python (PySpark, pandas, algorithms), Cloud (AWS, GCP, Airflow, Snowflake), and Behavioral rounds.

Can I practice for a specific company like Amazon or Google?

Yes. Our question database is tagged by company. You can ask the AI Interviewer to focus on questions frequently asked at specific companies, and it will pull from real reported interview data for that company.

How accurate is the AI scoring?

The Evaluator agent scores your answers against expert-calibrated benchmark answers stored in our database. It rates accuracy (1-10), depth (1-10), communication (1-10), and completeness (1-10), then delivers an overall hire/no-hire verdict with specific gap analysis.

Ready to Practice?

Join thousands of data engineers who used DataEngPrep to land roles at Amazon, Google, Databricks, and more.

Start Free AI Interview →Get the Full Vault ($21)

AI Mock Interview for Data Engineers

Practice with 3 specialized AI agents trained on 1,863+ real interview questions from Amazon, Google, Databricks, and 95+ more companies.

Start Free AI Interview →Browse 1,863+ Questions

Free: 5 messages/session · No credit card required

How the AI Mock Interview Works

Three AI agents, each specialized for a different phase of your interview preparation.

Interviewer Agent

FAANG Bar Raiser

✓Asks real FAANG-caliber questions one at a time
✓Pushes depth with adaptive follow-up questions
✓Delivers a hire/no-hire verdict every 3 questions
✓Covers SQL, Spark, System Design, Python, Cloud, Behavioral

Sample Session

Let's start with a common SQL round question. Explain the difference between ROW_NUMBER(), RANK(), and DENSE_RANK() window functions.

ROW_NUMBER assigns a unique sequential number. RANK assigns the same rank for ties but skips. DENSE_RANK doesn't skip...

Good foundation. Follow-up: You have a table with 10M rows and need to find the top 3 salaries per department. Which function would you use and why? Consider performance at scale.

Evaluator Agent

Hiring Committee

✓Scores answers on 4 axes: Accuracy, Depth, Communication, Completeness
✓Provides detailed gap analysis vs. expert benchmark
✓Shows the ideal 10/10 answer with code examples
✓Delivers Strong Hire / Hire / Lean Hire / No Hire verdict

Sample Session

Here's my answer to 'How do you handle data skew in Spark?': I would use salting to redistribute data...

Explainer Agent

Staff Engineer Mentor

✓Breaks complex concepts into clear, interview-ready explanations
✓Provides production-scale code examples
✓Gives interview answer templates you can use verbatim
✓Prepares follow-up questions the interviewer will likely ask

Sample Session

Explain the medallion architecture in a data lakehouse

Topics Covered in AI Interviews

Our AI agents are trained on every major data engineering interview domain.

487+

SQL & Databases

Window functions, CTEs, query optimization, BigQuery, Redshift

452+

Apache Spark

RDDs, DataFrames, Delta Lake, performance tuning, data skew

179+

System Design

Pipeline architecture, batch vs stream, medallion, fault tolerance

179+

Python & Coding

PySpark, pandas, algorithms, data structures, OOP patterns

179+

Cloud & Tools

AWS Glue, GCP Dataflow, Airflow, Snowflake, Terraform

144+

Behavioral

STAR method, leadership, conflict resolution, career goals

Frequently Asked Questions

How does the AI mock interview work?

What makes this different from ChatGPT for interview prep?

Is the AI mock interview free?

What topics does the AI interviewer cover?

Can I practice for a specific company like Amazon or Google?

How accurate is the AI scoring?

Ready to Practice?

Join thousands of data engineers who used DataEngPrep to land roles at Amazon, Google, Databricks, and more.

Start Free AI Interview →Get the Full Vault ($21)